Long-Form Generation › Putting It All Together: A Complete Pipeline

def long_form_generation_pipeline(
    topic: str,
    audience: str,
    required_topics: list,
    words_per_section: int = 500,
) -> dict:
    """
    Full long-form generation pipeline:
    1. Generate outline
    2. Initialize entity registry
    3. Generate each section with outline + prior context
    4. Add transitions between sections
    5. Evaluate coverage and coherence

    Returns structured document with metadata.
    """

    # Stage 1: Generate outline
    outline = generate_outline(
        topic, f"{len(required_topics) * words_per_section} words", audience
    )

    # Stage 2: Initialize tracking structures
    document_plan = DocumentPlan(
        topic=topic, thesis=f"Comprehensive coverage of {topic}"
    )
    registry = EntityRegistry()

    # Stage 3: Generate sections
    generated_sections = []
    previous_sections = []

    for i, section_title in enumerate(required_topics):
        section_content = generate_section(
            outline=outline,
            section_title=section_title,
            section_index=i + 1,
            previous_sections=previous_sections,
            words_per_section=words_per_section,
        )

        # Add transition if not first section
        if i > 0 and previous_sections:
            last_paragraph = previous_sections[-1].split("\n")[-1]
            transition = generate_transition(
                last_paragraph, section_title, f"Cover {section_title}"
            )
            section_content = transition + "\n\n" + section_content

        generated_sections.append(section_content)
        previous_sections.append(section_content)

    # Stage 4: Assemble and evaluate
    full_text = "\n\n".join(generated_sections)
    coverage = compute_topic_coverage(full_text, required_topics)
    coherence = compute_entity_transition_score(full_text)
    consistency_issues = check_numerical_consistency(full_text)

    return {
        "text": full_text,
        "sections": generated_sections,
        "coverage": coverage,
        "coherence_score": coherence,
        "consistency_issues": len(consistency_issues),
        "word_count": len(full_text.split()),
    }